# Redis

>[Redis (Remote Dictionary Server)](https://en.wikipedia.org/wiki/Redis) is an open-source in-memory storage, 
> used as a distributed, in-memory key–value database, cache and message broker, with optional durability. 
> Because it holds all data in memory and because of its design, `Redis` offers low-latency reads and writes, 
> making it particularly suitable for use cases that require a cache. Redis is the most popular NoSQL database, 
> and one of the most popular databases overall.

This page covers how to use the [Redis](https://redis.com) ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Redis wrappers.

## Installation and Setup

Install the Python SDK:

```bash
pip install redis
```

To run Redis locally, you can use Docker:

```bash
docker run --name langchain-redis -d -p 6379:6379 redis redis-server --save 60 1 --loglevel warning
```

To stop the container:

```bash
docker stop langchain-redis
```

And to start it again:

```bash
docker start langchain-redis
```

## Wrappers

All wrappers need a redis url connection string to connect to the database support either a stand alone Redis server
or a High-Availability setup with Replication and Redis Sentinels.

### Redis Standalone connection url
For standalone `Redis` server, the official redis connection url formats can be used as describe in the python redis modules
"from_url()" method [Redis.from_url](https://redis-py.readthedocs.io/en/stable/connections.html#redis.Redis.from_url)

Example: `redis_url = "redis://:secret-pass@localhost:6379/0"`

### Redis Sentinel connection url

For [Redis sentinel setups](https://redis.io/docs/management/sentinel/) the connection scheme is "redis+sentinel". 
This is an unofficial extensions to the official IANA registered protocol schemes as long as there is no connection url
for Sentinels available.

Example: `redis_url = "redis+sentinel://:secret-pass@sentinel-host:26379/mymaster/0"`

The format is  `redis+sentinel://[[username]:[password]]@[host-or-ip]:[port]/[service-name]/[db-number]`
with the default values of "service-name = mymaster" and "db-number = 0" if not set explicit.
The service-name is the redis server monitoring group name as configured within the Sentinel. 

The current url format limits the connection string to one sentinel host only (no list can be given) and
booth Redis server and sentinel must have the same password set (if used).

### Redis Cluster connection url

Redis cluster is not supported right now for all methods requiring a "redis_url" parameter.
The only way to use a Redis Cluster is with LangChain classes accepting a preconfigured Redis client like `RedisCache`
(example below).

### Cache

The Cache wrapper allows for [Redis](https://redis.io) to be used as a remote, low-latency, in-memory cache for LLM prompts and responses.

#### Standard Cache
The standard cache is the Redis bread & butter of use case in production for both [open-source](https://redis.io) and [enterprise](https://redis.com) users globally.

To import this cache:
```python
from langchain.cache import RedisCache
```

To use this cache with your LLMs:
```python
from langchain.globals import set_llm_cache
import redis

redis_client = redis.Redis.from_url(...)
set_llm_cache(RedisCache(redis_client))
```

#### Semantic Cache
Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends Redis as both a cache and a vectorstore.

To import this cache:
```python
from langchain.cache import RedisSemanticCache
```

To use this cache with your LLMs:
```python
from langchain.globals import set_llm_cache
import redis

# use any embedding provider...
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings

redis_url = "redis://localhost:6379"

set_llm_cache(RedisSemanticCache(
    embedding=FakeEmbeddings(),
    redis_url=redis_url
))
```

### VectorStore

The vectorstore wrapper turns Redis into a low-latency [vector database](https://redis.com/solutions/use-cases/vector-database/) for semantic search or LLM content retrieval.

To import this vectorstore:
```python
from langchain.vectorstores import Redis
```

For a more detailed walkthrough of the Redis vectorstore wrapper, see [this notebook](/docs/integrations/vectorstores/redis).

### Retriever

The Redis vector store retriever wrapper generalizes the vectorstore class to perform low-latency document retrieval. To create the retriever, simply call `.as_retriever()` on the base vectorstore class.

### Memory
Redis can be used to persist LLM conversations.

#### Vector Store Retriever Memory

For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](/docs/modules/memory/types/vectorstore_retriever_memory).

#### Chat Message History Memory
For a detailed example of Redis to cache conversation message history, see [this notebook](/docs/integrations/memory/redis_chat_message_history).
